Intelligent Selection Method for Gravity-suitable Area Based on Improved Support Vector Machine by the Genetic AlgorithmHe, Chuangxin; Zhang, Fengshuo; Yu, Xiaoyou; Zheng, Wei
doi: 10.1088/1742-6596/2637/1/012005pmid: N/A
The gravity-aided inertial navigation system (GAINS) can achieve precise positioning of underwater vehicles in a gravity-suitable area. However, there are generally shortcomings in the existing intelligent suitable area selection methods in terms of selecting gravity feature parameters and learning parameters. In this paper, an intelligent suitable area selection method is proposed based on an improved support vector machine by the genetic algorithm (GA-SVM) to address the aforementioned problems. Firstly, the genetic algorithm (GA) is utilized to independently pick out the optimal feature subset of 15 existing gravity feature parameters and obtain the optimal support vector machine (SVM) learning parameters while eliminating irrelevant redundant features, thus improving the classifier’s performance and generalization ability. Then, the SVM classifier is trained according to the optimal information output by GA, and the accuracy of the test set is 95.5%. Finally, the classifier is utilized to distinguish suitable and unsuitable areas in the application region to evaluate the proposed method’s performance. The TERCOM experiment in the suitable area resulted in an average positioning error of less than 170 m.
Hardware Acceleration Schemes for Convolutional Neural NetworksXie, Zhiqing; Jiang, Xue; Peng, Meihua; Yang, Ziheng
doi: 10.1088/1742-6596/2637/1/012013pmid: N/A
This paper presents a hardware acceleration design for convolutional neural networks. Floating-point fixed-point operations, pipeline interlayer parallel acceleration, and design space exploration are the three key areas of optimization, and optimized modules can be used to build various networks with convolutions according to specifications for the application scenario, thus achieving a universal design. The experimental results show that the optimization of hardware resources improves the speed and performance of the algorithm, and can withstand larger data volumes and higher real-time requirements. The system achieves an accuracy of 95.09% and an inference speed of 0.237 ms per image, with a high processing speed. As a result, convolutional neural networks may now be used in a wider variety of application scenarios and manage larger datasets and higher real-time demands thanks to the design solutions presented in this research.
Research and Optimization of Aircraft Remaining Oil Control System based on Fuzzy AlgorithmLiu, Chaohui; Liu, Taihui; Tan, Gangtong; Zhang, Wei
doi: 10.1088/1742-6596/2637/1/012025pmid: N/A
Aiming at the poor accuracy and stability of the existing aircraft remaining oil control system, an amount of oil remaining system for aircraft based on a fuzzy algorithm is proposed based on capacitance detection and mechanical detection, with the introduction of fuzzy algorithm optimized PID control technology. The system can form flight variables control system according to the error between the actual remaining oil and the ideal remaining oil and realize the stable control of the aircraft remaining oil. Furthermore, through targeted case analysis, the deviation of aircraft residual fuel before and after optimization is compared so as to further verify the reliability and feasibility of the designed aircraft residual fuel control system based on a fuzzy algorithm. In conclusion, the fuzzy algorithm is efficient in the optimization of the aircraft remaining oil control system.
Cooperative Spectrum Sensing Algorithm Based on Eigenvalue FusionGuo, Qianrui; Guo, Bin; Li, Xiangkun; Ma, Weijiao
doi: 10.1088/1742-6596/2637/1/012044pmid: N/A
A novel algorithm is introduced to improve collaborative spectrum sensing under low cognitive capabilities and insufficient signal-to-noise ratio. The algorithm is based on the difference of random matrix eigenvalues and uses the theory of random eigenvalues and the extreme distribution of the minimum eigenvalue. It makes use of the average, both arithmetic and geometric, as well as the minimum and maximum values of eigenvalues as the detection metric. It calculates the fusion power parameter through local energy spectrum sensing. Simulation results demonstrate that the algorithm outperforms the DMM algorithm and the NMME algorithm under users with low cognitive capabilities and Insufficient signal-to-noise ratio, making it more suitable for low signal-to-noise ratio environments.
Algorithm for Detecting Small Targets Based on Upgraded YOLOv5sTian, Chengjun; Liu, Haobo; Liu, Zhe; Yan, Yu; Zhang, Jintong
doi: 10.1088/1742-6596/2637/1/012043pmid: N/A
Small targets are discovered despite the challenges posed by the limited number of features, low resolution, difficulty in extracting discriminative characteristics, and ease of interference from external influences. A modest target detection technique based on enhanced YOLOv5s is suggested in this research. First, a test storey dedicated to tiny objects is appended to the characteristic fusion part of the algorithm to help the model efficiently acquire features with smaller sensory fields; second, to help the model find features more correctly, a cooperative attention method module is integrated into the characteristic extraction section to eliminate trait redundancy without sacrificing feature information; finally, the Bi-FPN network construction is combined to help the model to locate features more accurately. With the Bi-FPN construction, the characteristic combination process is modified to strengthen the examining capability of small objectives with a characteristic combination. The experimental results demonstrate that the enhanced algorithm increases average detection accuracy over the baseline YOLOv5s method by 6.3%, while also increasing small target detection accuracy and decreasing false and missed detection situations.
Differential evolutionary algorithm based on principal component analysis for the satellite bandwidth resource scheduling problemWang, Zihan; Wang, Dan; Zhang, Anqi; Yang, Liping
doi: 10.1088/1742-6596/2637/1/012055pmid: N/A
This paper focuses on the research of the communication satellite bandwidth resource scheduling problem. Satellite resource scheduling refers to adjusting the order of task execution within a certain scheduling time to complete as many satellite tasks as possible while satisfying bandwidth and time constraints. Traditional algorithms cannot meet the time requirements in the process of satellite communication resource scheduling. Therefore, this paper proposes a differential evolution algorithm based on principal component analysis (PCA) and combines it with the Lowest Horizontal Line Algorithm (LHLA) to solve this problem. The method was validated on two datasets at 20 dimensions and 40 dimensions, and compared with other traditional DE algorithms. The experimental results showed that the method obtained better solutions.
Robot path planning based on improved A* algorithmYu, Jintao; Gao, Zhenhua; Jiang, Mingze; Hou, Enqi
doi: 10.1088/1742-6596/2637/1/012008pmid: N/A
To address the issues of excessive polyline paths and the increased number of search nodes in the A* algorithm, a weighted WA* algorithm is proposed based on the A* algorithm. Firstly, a new distance function is introduced to reduce computational resource usage and improve search efficiency. Then, the heuristic function is improved by searching all feasible paths to find the shortest path. When the lengths of feasible paths are very similar, an additional value is added to the heuristic function along with weights to minimize frequent searches. Lastly, Bezier curves are introduced to enhance the smoothness of the global path. Simulation results show that the improved WA* algorithm reduces the number of turns, time consumption, search space, and turning angles by 25.0%, 65.4%, 53.5%, and 18.7%, respectively, compared to the A* algorithm. Compared to the bidirectional A* algorithm, these reductions are 7.6%, 34.4%, 43.5%, and 7.1%, respectively. In addition, the WA* algorithm uses Bezier curves to curve-fit the generated path points, making the path trajectory smoother.
End-to-end Remote Sensing Image Aircraft Target Detection and Fine-grained Recognition FrameworkMao, Jiaxing; Wang, Yifan; Guo, Zihao; Zhang, Xinyu; Yang, Yu
doi: 10.1088/1742-6596/2637/1/012037pmid: N/A
In view of the practical application of target detection and recognition tasks for remote sensing, an end-to-end aircraft target detection and fine-grained recognition framework is proposed. It can accurately and quickly implement detection and recognition in an end-to-end way. The main network of the framework adopts the design ideas of target detection and fine-grained recognition methods using candidate region extract and visual attention, making sure the accuracy of detection and recognition. Then, to solve the problem of high false detection rate and missed detection rate of densely arranged targets, we propose the re-detection mechanism. To minimize the large amounts of calculations of deep networks and improve real-time performance, we introduce depthwise separable convolution to optimize networks. Finally, a weight mapping idea based on transfer learning is adopted to solve the problem of data labeling and also helps the detection and fine-grained recognition. The results prove that the proposed framework has good robustness, versatility, and efficiency in aircraft detection and fine-grained recognition tasks.
Classification of Clavicle Fractures based on Multi-View FusionHuang, Yongfeng; Cao, Qingyu; Huang, Yongfeng; Tong, Hongfang
doi: 10.1088/1742-6596/2637/1/012033pmid: N/A
Clavicle fracture is a common shoulder injury. Clinical Allman classification divides clavicle fracture into middle fracture, distal fracture and proximal fracture. Different fracture types have corresponding treatment methods and different healing standards. The diagnosis of clavicle fractures can be misdiagnosed and missed by doctors due to blurring of the fracture line. In order to improve the diagnostic efficiency of clinicians and provide clearer treatment ideas, this paper establishes a two-stage clavicle-assisted diagnostic model. The first stage is based on 3D U-Net to segment the shoulder CT of normal clavicle and clavicle fracture in 3D, with dice coefficient reaching 0.9441, and then calculates the two-dimensional image information entropy of the image to select the key layers of the clavicle for classification of the segmented 3D image. The second stage of classification was performed to fuse the key layers data under the three views. The experimental results showed that the three-view fusion had a higher classification accuracy compared to the single-view slice, and the accuracy was improved by 1.3% to 93.4% compared to the best coronal classification, which showed that the two-stage classification method showed good classification effect and could help doctors improve the diagnostic efficiency.